Biological traits, rather than environment, shape detection curves of large vertebrates in neotropical rainforests

Abstract

Line transect surveys are widely used in Neotropical rainforests to estimate the population abundance of medium- and large-sized vertebrates. The use of indices such as encounter rate has been criticized because the probability of animal detection may fluctuate due to the heterogeneity of environmental conditions among sites. In addition, the morphological and behavioral characteristics (biological traits) of species affect their detectability. In this study, we compared the extent to which environmental conditions and species' biological traits bias abundance estimates in terra firme rainforests in French Guiana. The selected environmental conditions included both physical conditions and forest structure covariates, while the selected biological traits included the morphological and behavioral characteristics of species. We used the distance sampling method to model the detection probability as an explicit function of environmental conditions and biological traits and implemented a model selection process to determine the relative importance of each group of covariates. Biological traits contributed to the variability of animal detectability more than environmental conditions, which had only a marginal effect. Detectability was best for large animals with uniform or disruptive markings that live in groups in the canopy top. Detectability was worst for small, solitary, terrestrial animals with mottled markings. In the terra firme rainforests that represent ~80% of the Amazonia and Guianas regions, our findings support the use of relative indices such as the encounter rate to compare population abundance between sites in species-specific studies. Even though terra firme rainforests may appear similar between regions of Amazonia and the Guianas, comparability must be ensured, especially in forests disturbed by human activity. The detection probability can be used as an indicator of species' vulnerability to hunting and, thus, to the risk of local extinction. Only a few biological trait covariates are required to correctly estimate the detectability of the majority of medium- and large-sized vertebrates. Thus, a biological trait model could be useful in predicting the detection probabilities of rare, uncommon, or localized species for which few data are available to fit the detection function.

Phylogeny and species traits predict bird detectability

“…Understanding how variation in singing behaviour and signal transmission leads to imperfect detection is important for single species conservation and management (Kellner and Swihart 2014, Guillera-Arroita et al 2015, Alexander et al 2017, Guillera-Arroita 2017. There is growing evidence that species traits can be important predictors of detectability (Seoane et al 2005, Garrard et al 2013, Denis et al 2017 and that phylogenetic relationships can be utilized in joint community models that consider imperfect detection (Frishkoff et al 2017). Because avian singing behaviour and acoustic characteristics of songs are under directional selection (Thomas 1999), we hypothesized that species' detectability in birds might be predictable by species traits, phylogenetic relatedness, or both.…”

Birds of a feather flock together: Functionally similar vertebrates positively co‐occur in Guianan forests

“…). However, environmental conditions have already been shown to weakly shape medium‐ and large‐sized vertebrate large‐scale distribution in the Guiana Shield (Denis et al., , , ). All the studied species are indeed common across the sampled region, and their distribution only differs in relative abundance, thus not changing the presence‐only co‐occurrence pattern at these large scales.…”

“…Detection differences exist between species due to their biological traits (Denis et al. , Sólymos et al. ).…”

“…† Calls was computed based on expert opinions to assess the extent to which different clues help them to detect each species. We used the median value of experts' responses (five experienced observers and/or biologists) for each species and completed missing data for poorly observed species using a literature search (Denis et al 2017). We asked them to note the usefulness of cues from 0 (not useful) to 3 (useful in almost all detections).…”

“…Species co-occurrences were classified as negative, positive, or random from these probabilities at a chosen significance level (P). Detection differences exist between species due to their biological traits (Denis et al 2017(Denis et al , S olymos et al 2018. Smaller, solitary, and cryptic species are often less detected than larger, group-living, and non-cryptic species.…”